Information processing apparatus, vehicle, information processing method, and computer program product

ABSTRACT

According to an embodiment, an information processing apparatus includes one or more processors configured to: acquire a dynamic state related to traveling of a moving object entering an intersection; acquire intersection information indicating a configuration of the intersection; specify a reference route along which the moving object is predicted to travel at the intersection, based on the dynamic state and the intersection information; detect a speed control point that is a position included in the specified reference route; and generate a speed model representing a temporal change in a predicted speed of the moving object so that the speed at the speed control point is locally minimized, based on the dynamic state and the intersection information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2017-133683, filed on Jul. 7, 2017; theentire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to an informationprocessing apparatus, a vehicle, an information processing method, and acomputer program product.

BACKGROUND

For example, in a field of automatic driving or the like, in a casewhere a vehicle travels at an intersection, it is required to predicthow other vehicles travel. However, at the intersection, the vehiclescomplicatedly change the speeds. For this reason, it has been difficultfor the vehicle to predict how other vehicles travel.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a vehicle according to an embodiment;

FIG. 2 is a configuration diagram of an information processing apparatusaccording to an embodiment;

FIG. 3 is a functional configuration diagram of a processing circuit;

FIG. 4 is a diagram illustrating an example of reference routes and thelike;

FIG. 5 is a diagram illustrating an example of a speed control point;

FIG. 6 is a flowchart illustrating a flow of processes of theinformation processing apparatus according to the embodiment;

FIG. 7 is a diagram illustrating an example of a first speed model and asecond speed model;

FIG. 8 is a flowchart illustrating a flow of a generation process of thefirst speed model;

FIG. 9 is a diagram illustrating an optimization process of the firstspeed model;

FIG. 10 is a flowchart illustrating a flow of a generation process ofthe second speed model;

FIG. 11 is a view illustrating a speed control point according toModified Example 1; and

FIG. 12 is a diagram illustrating a speed control point according toModified Example 2.

DETAILED DESCRIPTION

According to an embodiment, an information processing apparatus includesone or more processors configured to: acquire dynamic state related totraveling of a moving object entering an intersection; acquireintersection information indicating a configuration of the intersection;specify a reference route along which the moving object is predicted totravel at the intersection, based on the dynamic state and theintersection information; detect a speed control point that is aposition included in the specified reference route; and generate a speedmodel representing a temporal change in a predicted speed of the movingobject so that the speed at the speed control point is locallyminimized, based on the dynamic state and the intersection information.

Hereinafter, embodiments will be described with reference to theaccompanying drawings. An information processing apparatus 10 accordingto an embodiment accurately predicts a speed of a moving object at anintersection. The moving object is, for example, another vehicle.

FIG. 1 is a diagram illustrating a vehicle 100 according to theembodiment. The vehicle 100 includes the information processingapparatus 10. The information processing apparatus 10 is, for example,an apparatus including a dedicated or general purpose computer. In theinformation processing apparatus 10, an information processing functionmay not be mounted on the vehicle 100 but may be mounted on anotherapparatus such as a cloud. The information processing apparatus 10 maybe provided not in the vehicle 100 but in another type of a movingobject such as a robot or a drone. The vehicle 100 may be, for example,a normal vehicle traveling through driving operations by a person or maybe an automatically driven vehicle capable of automatically travelingwithout intervention by driving operations by a person (capable ofautonomously traveling).

FIG. 2 is a diagram illustrating an example of a configuration of theinformation processing apparatus 10 according to the embodiment. Theinformation processing apparatus 10 includes an input unit 12, a displayunit 14, a sensor unit 16, a communication unit 18, a vehicle controlunit 20, and a processing unit 30.

The input unit 12 receives instructions from a user and informationinput. The input unit 12 is, for example, an operation panel, a pointingdevice such as a mouse or a trackball, or an input device such as akeyboard.

The display unit 14 displays various kinds of information. The displayunit 14 is, for example, a display device such as a liquid crystaldisplay.

The sensor unit 16 includes one or more types of sensors for detecting aposition, speed, acceleration, angular speed, or the like of thesurrounding moving objects (for example, other vehicles). For example,the sensor unit 16 may include a sonar that detects surrounding movingobjects with sound waves. For example, the sensor unit 16 may include astereoscopic camera for acquiring the position in the depth direction ofthe surrounding moving object. For example, the sensor unit 16 mayinclude a millimeter wave radar for measuring a distance to thesurrounding moving object or a LIDAR sensor for detecting a position(for example, a distance and angle from the own vehicle) of thesurrounding moving object. The sensor unit 16 may include sensors otherthan these sensors in order to detect the position, speed, acceleration,angular speed, and the like of the surrounding moving object (forexample, another vehicle).

The communication unit 18 is an interface that communicates informationwith external devices through, for example, wireless communication. Thecommunication unit 18 may perform wireless communication with othervehicles, roadside devices, or the like. The communication unit 18 mayaccess a server or the like via a network.

The vehicle control unit 20 controls a driving mechanism for driving theown vehicle. For example, in a case where the vehicle 100 is anautomatically driven vehicle, the surrounding situation is determinedbased on the information obtained from the sensor unit 16 and otherinformation, and an accelerating amount, a braking amount, a steeringangle, and the like are controlled. In a case where the vehicle 100 is anormal vehicle traveling through driving operations by a person, thevehicle control unit 20 controls the accelerating amount, the brakingamount, the steering angle, and the like according to operationinformation.

The processing unit 30 is, for example, a dedicated or general purposecomputer. The processing unit 30 manages and controls the input unit 12,the display unit 14, the sensor unit 16, the communication unit 18, andthe vehicle control unit 20.

The processing unit 30 includes a processing circuit 32 and a storagecircuit 34. The processing circuit 32 and the storage circuit 34 areconnected to each other via a bus. The processing circuit 32 isconnected to the input unit 12, the display unit 14, the sensor unit 16,the communication unit 18, and the vehicle control unit 20, for example,via a bus.

The processing circuit 32 is, for example, one or a plurality ofprocessors, and reads a program from the storage circuit 34 and executesthe program to implement a function corresponding to the program. In thepresent embodiment, the processing circuit 32 executes a predictionprogram for predicting the speed of the surrounding moving object at theintersection. The processing circuit 32 in the state of reading andexecuting this prediction program includes each component illustrated inthe processing circuit 32 in FIG. 2. That is, the processing circuit 32executes the prediction program to function as a dynamic stateacquisition unit 42, an intersection information acquisition unit 44, adirection prediction unit 46, a route specification unit 48, a controlpoint detection unit 50, a model generation unit 52, a trajectorycalculation unit 54, and a presentation unit 56. Details of thesecomponents will be described later.

The term “processor” denotes, for example, a central processing unit(CPU), a graphical processing unit (GPU), an application specificintegrated circuit (ASIC), a programmable logic device (for example, asimple programmable logic device (SPLD), a complex programmable logicdevice (CPLD), and a field programmable gate array (FPGA)), or the like.The processor implements a function by reading and executing a programstored in the storage circuit 34. Instead of storing the program in thestorage circuit 34, the program may be configured to be directlyincorporated in circuit of the processor. In this case, the processorimplements the function by reading and executing the programincorporated in the circuit.

The storage circuit 34 stores a program for allowing the processingcircuit 32 to function as the dynamic state acquisition unit 42, theintersection information acquisition unit 44, the direction predictionunit 46, the route specification unit 48, the control point detectionunit 50, the model generation unit 52, the trajectory calculation unit54 and the presentation unit 56. The storage circuit 34 stores data andthe like accompanied with each processing function performed by theprocessing circuit 32, as necessary.

For example, the storage circuit 34 is a semiconductor memory elementsuch as a random access memory (RAM) or a flash memory, a hard disk, anoptical disk, or the like. Some or all of the functions of the storagecircuit 34 may be implemented by a storage device outside the processingunit 30. The storage circuit 34 may be a storage medium that stores ortemporarily stores a program transmitted and downloaded via a local areanetwork (LAN), the Internet, or the like. The storage circuit 34 may beconfigured with one storage medium or may be configured with a pluralityof storage media. The storage circuit 34 may be configured with aplurality of types of storage media.

FIG. 3 is a diagram illustrating a functional configuration of theprocessing circuit 32. At the intersection, the processing circuit 32executes a prediction program for predicting the speed of the movingobject which is a surrounding other vehicle or the like. The processingcircuit 32 that has executed the prediction program includes the dynamicstate acquisition unit 42, the intersection information acquisition unit44, the direction prediction unit 46, the route specification unit 48,the control point detection unit 50, the model generation unit 52, thetrajectory calculation unit 54, and the presentation unit 56.

Herein, the intersection is an area where a plurality of roads(traveling roads of moving objects) intersect. A moving object that hasentered the intersection is to turn right or left and progress toanother road or is to go straight to progress along the road. In thepresent embodiment, the moving object is a vehicle. However, the movingobject is not limited to a vehicle, but the moving object may be a robotor a drone moving along a traveling road.

The dynamic state acquisition unit 42 specifies the moving objectentering the intersection as a target for speed prediction. For example,the dynamic state acquisition unit 42 specifies another vehicle beforeentering the intersection as a target moving object. In addition, forexample, the dynamic state acquisition unit 42 may specify anothervehicle that is passing through the intersection as a target movingobject.

Then, the dynamic state acquisition unit 42 acquires a dynamic state onthe travel of the target moving object. For example, the dynamic stateacquisition unit 42 acquires observation data such as the position,speed, and acceleration of the target moving object as dynamic state ofthe target moving object at regular time intervals. The dynamic stateacquisition unit 42 acquires the dynamic state based on the informationdetected by the sensor unit 16.

The intersection information acquisition unit 44 acquires intersectioninformation indicating the configuration of the intersection where thetarget moving object enters. The intersection information acquisitionunit 44 may read the intersection information stored in the storagecircuit 34 or acquire the intersection information from an externalserver or the like through the communication unit 18.

As illustrated in FIG. 4, for example, the intersection informationincludes information on the shape and the like of the intersection, suchas a plurality of reference routes, intersection angles, radius ofcorners, roadway widths, and distances from sidewalk-roadway boundarylines. Furthermore, the intersection information may also includeinformation on the signs of the intersection, such as contents, displaypositions, and the like of crosswalk signs and road signs at theintersection.

The reference route represents a travel route of the moving objectwithin the intersection. The reference route may be a point sequencerepresenting positions or may be a curve parameter such as a splinecurve or a clothoid curve. The intersection information includesinformation on all reference routes along which traveling can be allowedat the intersection. For example, the intersection information on theintersection where two roads intersect includes reference routes ofgo-straight, turn-right, and turn-left for each inflow port.

The intersection angle represents an angle of the two intersectingroads. The radius of the corner represents a radius of the arc formed atthe corner at the intersection. The radius of the corner may be a valueobtained by subtracting a value of ½ of the roadway width from theradius of curvature at the position at which the curvature of thereference route is maximized. The intersection information acquisitionunit 44 may calculate an approximate value of the radius of the cornerby using other methods. The distance from the sidewalk-roadway boundaryline represents a distance from a boundary line between a sidewalk and aroadway to the reference route.

The direction prediction unit 46 predicts in which direction the targetmoving object travels within the intersection. For example, thedirection prediction unit 46 predicts whether the target moving objectgoes straight, turns right, or turns left. For example, the directionprediction unit 46 recognizes the blinking of the blinker provided inthe target moving object based on the image of the target moving objectimaged by the sensor unit 16 and predicts whether the target movingobject goes straight, turns right, or turns left. The directionprediction unit 46 may predict whether the target moving object goesstraight, turns right, or turns left based on the information acquiredfrom the target moving object through inter-vehicle communication.Alternatively or additionally, the direction prediction unit 46 maypredict whether the target moving object goes straight, turns right, orturns left, according to a change in speed and a change in angular speedof the target moving object.

The route specification unit 48 specifies a reference route along whichthe target moving object is predicted to travel at the intersectionbased on the dynamic state of the target moving object, a predictionresult of the direction in which the target moving object is to travel,and the intersection information. For example, the route specificationunit 48 specifies, from among the plurality of reference routes includedin the intersection information, one reference route along which thetarget moving object is predicted to travel based on a prediction resultof the position of the target moving object and the direction in whichthe target moving object is to progress. In a case where the directionin which the target moving object is to travel cannot be predicted, theroute specification unit 48 may specify two or more reference routesalong which the target moving object may travel.

The control point detection unit 50 detects a speed control point whichis a position included in the reference route specified by the routespecification unit 48. For example, the speed control point represents aposition at which it is predicted that a positive or negative sign ofthe acceleration of the target moving object is reversed in thereference route. For example, as illustrated in FIG. 5, the controlpoint detection unit 50 detects, as a speed control point, a positionhaving the maximum curvature in the specified reference route. In thiscase, the control point detection unit 50 analyzes the shape of thespecified reference route to detect the position having the maximumcurvature.

The model generation unit 52 generates a speed model representing atemporal change in the predicted speed of the target moving object basedon the specified reference route, the dynamic state, the intersectioninformation and the speed control point. The model generation unit 52includes a go-straight model generation unit 62 and a turn-left/rightmodel generation unit 64.

In a case where it is determined that the target moving object goesstraight at the intersection, the go-straight model generation unit 62generates a speed model based on the specified reference route, thedynamic state and the intersection information. For example, thego-straight model generation unit 62 generates a model in which thespeed approaches a stable speed by causing the acceleration to begradually close to zero. For example, the go-straight model generationunit 62 may generate a model representing a constant speed motion, aconstant acceleration motion, or the like. For example, the go-straightmodel generation unit 62 calculates, as a speed model, a function withtime as a variable.

In a case where it is determined that the target moving object turnsleft or turns right at the intersection, the turn-left/right modelgeneration unit 64 generates a speed model in which the speed at thespeed control point is locally minimized based on the specifiedreference route, dynamic state and intersection information. The term“local minimum” denotes a peak point of a waveform that has a peakdownward.

Specifically, the turn-left/right model generation unit 64 generates afirst speed model representing a temporal change in the predicted speedof the target moving object up to the speed control point and a secondspeed model representing the temporal change in the predicted speed ofthe target moving object after the speed control point. For example, theturn-left/right model generation unit 61 calculates a function with timeas a variable and a second speed model. For example, the turn-left/rightmodel generation unit 64 calculates cubic functions with time as avariable as a first speed model and a second speed model. By generatingthe speed model, the model generation unit 52 can predict the speed ofthe target moving object at an arbitrary time in the future.

The trajectory calculation unit 54 calculates a predicted movementtrajectory of the target moving object based on the speed modelgenerated by the model generation unit 52 and the specified referenceroute. The presentation unit 56 draws the predicted movement trajectorycalculated by the trajectory calculation unit 54 on, for example, mapinformation and causes the display unit 14 to display the predictedmovement trajectory. The presentation unit 56 may generate the mapinformation based on each information detected by the sensor unit 16 ormay acquire the map information from a server or the like via a network.By causing the predicted movement trajectory to be displayed on thedisplay unit 14, the presentation unit 56 can present how the targetmoving object moves to the driver of the own vehicle.

FIG. 6 is a flowchart illustrating the flow of processes of theinformation processing apparatus 10 according to the embodiment. Theinformation processing apparatus 10 executes the processes in the flowillustrated in FIG. 6.

First, in step S111, the information processing apparatus 10 specifiesone of the moving objects entering the intersection as a target movingobject. Subsequently, in step S112, the information processing apparatus10 acquires dynamic state of the target moving object. The informationprocessing apparatus 10 acquires the position, speed, and accelerationof the target moving object, for example, at predetermined intervals.

Subsequently, in step S113, the information processing apparatus 10acquires the intersection information representing the configuration ofthe intersection where the target moving object enters. For example, theinformation processing apparatus 10 acquires intersection informationincluding a plurality of reference routes, intersection angles, radiusof corners, roadway widths, distances from the sidewalk-roadway boundaryline.

Subsequently, in step S114, the information processing apparatus 10predicts in which direction the target moving object progresses withinthe intersection. For example, the information processing apparatus 10predicts whether the target moving object goes straight, turns right, orturns left within the intersection.

Subsequently, in step S115, the information processing apparatus 10specifies the reference route along which the target moving object ispredicted to travel at the intersection based on the dynamic state ofthe target moving object, a prediction result of the direction in whichthe target moving object is to travel, and the intersection information.The information processing apparatus 10 specifies, from among theplurality of reference routes included in the intersection information,one reference route along which the target moving object is predicted totravel based on a prediction result of the position of the target movingobject and the direction in which the target moving object is toprogress.

Subsequently, in step S116, the information processing apparatus 10determines whether to go straight or turn left or right. In the case ofgoing straight, the information processing apparatus 10 proceeds to theprocess of step S117. In the case of turning left or turning right, theinformation processing apparatus 10 proceeds to the process of stepS116.

In step S117, the information processing apparatus 10 generates a speedmodel of the target moving object in the case of going straight at theintersection based on the specified reference route, the dynamic state,and the intersection information. For example, the informationprocessing apparatus 10 generates a model where the speed approaches astable speed by allowing the acceleration to be gradually close to zero.For example, the information processing apparatus 10 may generate amodel representing a constant speed motion, a constant accelerationmotion, or the like. When the process of step S117 is completed, theinformation processing apparatus 10 proceeds to the process of stepS120.

In step S110, the information processing apparatus 10 detects a speedcontrol point which is a position included in the specified referenceroute. For example, the control point detection unit 50 detects, as aspeed control point, a position at which it is predicted that thepositive or negative sign of the acceleration of the target movingobject is reversed in the reference route. For example, the controlpoint detection unit 50 detects, as a speed control point, a positionhaving the maximum curvature in the specified reference route.

Subsequently, in step S119, the information processing apparatus 10generates a speed model of the target moving object in the case ofturning left or turning right at the intersection based on the specifiedreference route, the speed control point, the dynamic state, and theintersection information. In this case, the information processingapparatus 10 generates a speed model in which the speed at the speedcontrol point is locally minimized.

In the process of step S119, the information processing apparatus 10executes the processes of step S119-1 and step S119-2. Specifically,first, in step S119-1, the information processing apparatus 10 generatesa first speed model representing a temporal change in the predictedspeed of the target moving object up to the speed control point.Subsequently, in step S119-2, the information processing apparatus 10generates a second speed model representing a temporal change in thepredicted speed of the target moving object after the speed controlpoint.

As illustrated in FIG. 7, in the present embodiment, the first speedmodel is a cubic function (f(t)=at³+bt²+ct+d) with time (t) as avariable. In the present embodiment, the second speed model is a cubicfunction (g(t)=a′t³+b′t²+c′t+d′) with time (t) as a variable.

At the passing time (t_(min)) through the speed control point, the firstspeed model and the second speed model represent the same speed and thepredicted speed is locally minimized (v_(min)). The method of generatingthe first speed model will be further described with reference to FIG.8. The method of generating the second speed model will be furtherdescribed with reference to FIG. 10.

When the process of step S119 is completed, the information processingapparatus 10 proceeds to the process of step S120. In step S120, theinformation processing apparatus 10 calculates a predicted movementtrajectory of the target moving object based on the generated speedmodel and the specified reference route. Subsequently, in step S121, theinformation processing apparatus 10 draws the calculated predictedmovement trajectory on, for example, the map information, and allows thedisplay unit 14 to display the predicted movement trajectory. When theprocess of step S121 is completed, the information processing apparatus10 ends the process for the target moving object.

FIG. 8 is a flowchart illustrating the flow of the generation process ofthe first speed model. The model generation unit 52 generates the firstspeed model with the flow of the process illustrated in FIG. 8.

First, in step S131, the model generation unit 52 calculates the initialestimated speed at the speed control point. For example, the modelgeneration unit 52 calculates the initial estimated speed of the targetmoving object at the speed control point based on the inflow speed, theintersection angle, the radius of the corner, and the distance from thesidewalk-roadway boundary line.

For example, the model generation unit 52 sets, as the initial estimatedspeed, a value obtained by adding all values obtained by multiplying theinflow speed, the intersection angle, the radius of the corner, and thedistance from the sidewalk-roadway boundary line by their correspondingcoefficients and further adding a constant to the addition result. Morespecifically, the model generation unit 52 calculates the followingMathematical Formula (1).x=a ₁ X ₁ +a ₂ X ₂ +a ₃ X ₃ +a ₄ X ₄ +a ₅  (1)

In the Mathematical Formula (1), x indicates the initial estimated speedof the target moving object at the speed control point. X₁ indicates theinflow speed. X₂ indicates the intersection angle. X₃ indicates theradius of the corner. X₄ indicates the distance from thesidewalk-roadway boundary line.

Furthermore, a₁, a₂, a₃, and a₄ are coefficients and is a constant.These coefficients and constant are values estimated from data obtainedby actually measuring the moving object passing through theintersection. These coefficients and constant are included in, forexample, the intersection information. The model generation unit 52acquires these coefficients and constant from the intersectioninformation.

The inflow speed is a speed of the target moving object at the time ofentering the intersection. Specifically, the inflow speed is a speed ofthe target moving object at the inflow port of the intersection. Forexample, the model generation unit 52 acquires observation data of thespeed of the target moving object at the inflow port of the intersectionfrom among the dynamic state acquired by the dynamic state acquisitionunit 42. The non-patent literature described above discloses that theminimum speed at the time of turning left or turning right at theintersection can be estimated based on the inflow speed, theintersection angle, the radius of the corner, and distance from thesidewalk-roadway boundary line.

Subsequently, in step S132, the model generation unit 52 acquires aposition of the target moving object at a reference time. The referencetime is a time which is a reference for the speed estimation of thetarget moving object. The reference time may be, for example, thecurrent time or may be the time when the position, speed, acceleration,or the like of the target moving object was last observed. For example,the model generation unit 52 acquires observation data of the positionof the target moving object at the reference time from among the dynamicstate acquired by the dynamic state acquisition unit 42.

Subsequently, in step S133, the model generation unit 52 calculates aroute length from the position of the target moving object at thereference time to the speed control point, based on the specifiedreference route. That is, the model generation unit 52 calculates amovement distance in a case where the target moving object moves on thespecified reference route from the position of the target moving objectat the reference time to the speed control point.

Subsequently, in step S134, the model generation unit 52 acquiresobservation data of the speed of the target moving object at least onepoint in time before the reference time based on previous dynamic stateof the target moving object. For example, the model generation unit 52acquires the observation data of the speed of the target moving objectfrom the dynamic state of N (N is an integer of 1 or more) samplesbefore the reference time.

Subsequently, in step S135, the model generation unit 52 optimizes thefirst speed model so that errors of the observation data of the initialestimated speed of the moving object at the speed control point and thespeed of the moving object at the at least one point in time becomesmall. For example, as illustrated in FIG. 9, the model generation unit52 optimizes the coefficients (a, b, c, d) in the first speed modelrepresented by the cubic function (f(t)=at³+bt²+ct+d) so that squareerrors of the observation data of the initial estimated speed at thespeed control point and the speed of the moving object at least onepoint in time are minimized.

Herein, the model generation unit 52 optimizes the first speed modelunder a constraint condition that the speed at the speed control pointis locally minimized. More specifically, the model generation unit 52determines the time at which the differential value of the first speedmodel becomes zero and the differential value is reversed from negativeto positive as a time at which the speed is locally minimized. Then, themodel generation unit 52 optimizes the first speed model so that theintegral value from the reference time in the first speed model to thetime at which the speed is locally minimized is matched with the routelength calculated in step S133.

The model generation unit 52 optimizes the first speed model so thatthere is no other extremum between the reference time and the speedcontrol point. More specifically, the model generation unit 52 optimizesthe first speed model so that there is no point at which thedifferential value is zero between the reference time and the time atthe speed control point.

In a case where the observation data of the speed of the target movingobject at the reference time has been acquired, the model generationunit 52 may optimize the first speed model so that the speed at thereference time is matched with the observation data.

Furthermore, in a case where the observation data of the acceleration ofthe target moving object at the reference time has been acquired, themodel generation unit 52 may optimize the first speed model so that theacceleration at the reference time is matched with the observation data.More specifically, the model generation unit 52 optimizes the firstspeed model so that the differential value at the reference time of thefirst speed model is matched with the observation data of theacceleration at the reference time.

Subsequently, in step S136, the model generation unit 52 determineswhether or not the acceleration (differential value) between thereference time and the time at the speed control point obtained from theoptimized first speed model is within a predetermined range. Forexample, the model generation unit 52 determines whether or not theabsolute value of the acceleration obtained from the first speed modelexceeds a general range of the acceleration at the time of decelerationof the vehicle.

When the acceleration is not within the predetermined range (No in stepS136), the model generation unit 52 proceeds to the process of stepS137. Then, in step S137, the model generation unit 52 changes the speedcontrol point. For example, in a case where the absolute value of theacceleration obtained from the first speed model exceeds general rangeof the acceleration at the time of deceleration of the vehicle, themodel generation unit 52 shifts the speed control point by apredetermined distance to a forward position on the reference route.Then, the model generation unit 52 proceeds to the process of step S135again to optimize the first speed model.

Then, in a case where the acceleration is within the predetermined range(Yes in step S136), the model generation unit 52 ends the generationprocess of the first speed model.

FIG. 10 is a flowchart illustrating the flow of the generation processof the second speed model. In a case where the second speed model isrepresented by a cubic function, the model generation unit 52 generatesthe second speed model with the flow of the process illustrated in FIG.10.

First, in step S141, the model generation unit 52 obtains the time andthe speed at the speed control point based on the first speed model.

Subsequently, in step S142, the model generation unit 52 determines thestable speed. The stable speed is a speed of a moving object in a casewhere the moving object moves at a constant speed on a road (travelingroad) (in a case where the moving object moves at the acceleration ofzero). For example, the stable speed may be a legal speed or may be avalue estimated from data obtained by actually measuring a moving objectpassing through the intersection.

Subsequently, in step S143, a first coefficient of the second speedmodel represented by a cubic function is calculated. The firstcoefficient is a coefficient of a cubic term of the variable.

For example, the model generation unit 52 calculates the firstcoefficient of the second speed model based on the outflow speed and thedistance from the sidewalk-roadway boundary line. For example, the modelgeneration unit 52 sets, as the first coefficient, a value obtained byadding values obtained by multiplying the outflow speed and the distancefrom the sidewalk-roadway boundary line by their correspondingcoefficients and further adding a constant to the addition result. Morespecifically, the following Mathematical Formula (2) is calculated.a′=b ₁ Y ₁ +b ₂ Y ₂ +b ₃  (2)

In the Mathematical Formula (2), a′ indicates the first coefficient ofthe second speed model. Y₁ indicates the outflow speed. Y₂ indicates thedistance from the sidewalk-roadway boundary line.

b₁ and b₂ are coefficients and b₃ is a constant. These coefficients andconstant are values estimated from the data obtained by actuallymeasuring the moving object passing through the intersection. Thesecoefficients and constant are included in, for example, the intersectioninformation. The model generation unit 52 acquires these coefficientsand constant from the intersection information.

The outflow speed may be t stable speed calculated in step S142. Thenon-patent literature (Axel Wolfermann, Wael K. M. Alhajyaseen, HidekiNakamura, “MODELING SPEED PROFILES OF TURNING VEHICLES AT SIGNALIZEDINTERSECTIONS”, International Conference on Road Safety and Simulation(RSS2011)) discloses that the first coefficient of the second speedmodel can be estimated based on the outflow speed and the distance fromthe sidewalk-roadway boundary line.

Subsequently, in step S144, the model generation unit 52 calculates thesecond speed model which is a cubic function based on the time at thespeed control point, the speed at the speed control point, the stablespeed, and the first coefficient. The model generation unit 52 canuniquely determine the second speed model which is a cubic function byusing the time at the speed control point, the speed at the speedcontrol point, the stable speed, and the first coefficient.

When step S144 is ended, the model generation unit 52 ends thegeneration process of the second speed model.

FIG. 11 is a diagram illustrating a speed control point according toModified Example 1. Instead of detecting the position having the maximumcurvature as the speed control point, the control point detection unit50 may detect a center point within the intersection of the referenceroutes as the speed control point. For example, the control pointdetection unit 50 detects, as the speed control point, a center pointbetween the position at which the curvature on the inflow port side ofthe reference route becomes equal to or larger than a predeterminedvalue and the position at which the curvature on the outflow port sideof the reference route becomes smaller than the predetermined value.

For example, in a case where the reference route is an arc and thecurvature is substantially constant, the center point within theintersection of the reference route becomes the position at which thepositive or negative sign of the acceleration of the moving object isreversed. Therefore, in such a case, the control point detection unit 50can accurately detect the position at which the positive or negativesign of the acceleration of the moving object is reversed by setting thecenter point within the intersection of the reference route as the speedcontrol point.

FIG. 12 is a diagram illustrating a speed control point according toModified Example 2. Instead of detecting the position having the maximumcurvature as the speed control point, the control point detection unit50 may detect, as the speed control point, a position before thecrosswalk by a predetermined distance in the reference route. Forexample, the control point detection unit 50 acquires the content anddisplay position of the road sign included in the intersectioninformation and detects the position before the crosswalk by thepredetermined distance.

For example, in a case where the moving object passes through ahigh-traffic intersection or an intersection with poor view, the speedof the moving object is minimized just before the crosswalk. Therefore,in such a case, the control point detection unit 50 sets, as the speedcontrol point, a position before the crosswalk by a predetermineddistance. With this, the control point detection unit 50 can accuratelydetect the position at which the positive or negative sign of theacceleration of the moving object is reversed. The distance from thecrosswalk to the speed control point may be included in the intersectioninformation or may be registered in advance in the control pointdetection unit 50.

As described above, the information processing apparatus 10 according tothe present embodiment specifies the reference route along which thetarget moving object is predicted to travel and detects the speedcontrol point based on the specified reference route. Then, theinformation processing apparatus 10 generates a speed model representingthe temporal change in the predicted speed of the target moving objectso that the speed at the speed control point is locally minimised.Therefore, according to the information processing apparatus 10, it ispossible to accurately calculate the predicted speed of the targetmoving object traveling at the intersection.

The programs executed by the information processing apparatuses 10according to the embodiments and the modifications described above maybe stored on a computer connected to a network such as the Internet anddownloaded via the network so that the program is provided. The programsexecuted by the information processing apparatuses 10 according to theembodiments and the modifications described above may be provided ordistributed via a network such as the Internet. The programs executed bythe information processing apparatuses 10 according to the embodimentsand the modifications described above may be provided in a form of beingincorporated in advance into a nonvolatile recording medium such as aROM.

Furthermore, each of the embodiments and modifications described abovemay be arbitrarily combined.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. An information processing apparatus, comprising:processing circuitry configured to acquire a dynamic state related totraveling of a moving object entering an intersection, the dynamic stateincluding observation data of a speed of the moving object at least atone point in time; acquire intersection information indicating aconfiguration of the intersection; specify a reference route along whichthe moving object is predicted to travel at the intersection, based onthe dynamic state and the intersection information; detect a speedcontrol point that is a position included in the specified referenceroute; generate, based on the dynamic state and the intersectioninformation, a first speed model representing a temporal change in apredicted speed of the moving object up to the speed control point andhaving the speed at the speed control point being locally minimized, anda second speed model representing a temporal change in the predictedspeed of the moving object after the speed control point and having thespeed at the speed control point being locally minimized; and generateand display, on a display of a vehicle, a predicted trajectory of themoving object, based on the first speed model and the second speedmodel, wherein in the generating of the first speed model, theprocessing circuitry is further configured to estimate an initialestimated speed of the moving object at the speed control point; andoptimize the first speed model to reduce an error with respect to theinitial estimated speed of the moving object at the speed control pointand the observation data of the speed of the moving object at least atthe one point in time.
 2. The apparatus according to claim 1, whereinthe processing circuitry is further configured to detect, as the speedcontrol point, a position at which a positive or negative sign of anacceleration of the moving object is reversed.
 3. The apparatusaccording to claim 1, wherein the processing circuitry is furtherconfigured to detect, as the speed control point, a position at which acurvature in the reference route is maximized.
 4. The apparatusaccording to claim 1, wherein the processing circuitry is furtherconfigured to detect, as the speed control point, a center point in thereference route.
 5. The apparatus according to claim 1, wherein theprocessing circuitry is further configured to detect, as the speedcontrol point, a position before a crosswalk by a predetermined distancein the reference route.
 6. The apparatus according to claim 1, whereinthe processing circuitry is further configured to optimize the firstspeed model under a constraint condition that a route length of thereference route between a position of the moving object at a referencetime and the speed control point is matched with an integral value ofthe first speed model from the reference time to a time at which thespeed is locally minimized.
 7. The apparatus according to claim 6,wherein the first speed model is a cubic function with time as avariable, and the processing circuitry is further configured to optimizethe first speed model under a constraint condition that there is noother extremum from the reference time to the time at which the speed islocally minimized.
 8. The apparatus according to claim 7, wherein theprocessing circuitry is further configured to optimize the first speedmodel so that a square error is minimized under a constraint conditionwith respect to observation data of an initial estimated speed of themoving object at the speed control point and the speed of the movingobject at the at least one point in time.
 9. The apparatus according toclaim 1, wherein the processing circuitry is further configured to:acquire an inflow speed, the inflow speed being a speed of the movingobject at the time of entering the intersection; acquire an intersectionangle at the intersection, a radius of a corner, and a distance from asidewalk-roadway boundary line on the reference route; and calculate theinitial estimated speed of the moving object at the speed control pointbased on the inflow speed, the intersection angle, the radius of thecorner and the distance from the sidewalk-roadway boundary line.
 10. Theapparatus according to claim 1, wherein, when an acceleration calculatedfrom the optimized first speed model is outside a predetermined range,the processing circuitry is further configured to change the position ofthe speed control point and optimizes the first speed model again. 11.The apparatus according to claim 1, wherein the processing circuitry isfurther configured to generate the second speed model based on a stablespeed at which an acceleration of the moving object is zero and a timeand speed of the moving object at the time of passing the speed controlpoint calculated from the optimized first speed model.
 12. The apparatusaccording to claim 11, wherein the second speed model is a cubicfunction having time as a variable, and the processing circuitry isfurther configured to calculate a coefficient of a cubic term of thevariable in the second speed model based on the stable speed and adistance from the sidewalk-roadway boundary line.
 13. The apparatusaccording to claim 1, the processing circuitry is further configured tocontrol a driving mechanism to drive a vehicle based on the first speedmodel and the second speed model.
 14. A vehicle equipped with theinformation processing apparatus according to claim
 1. 15. Aninformation processing method, comprising: acquiring a dynamic staterelating to traveling of a moving object entering an intersection, thedynamic state including observation data of a speed of the moving objectat least at one point in time; acquiring intersection informationindicating a configuration of the intersection; specifying a referenceroute along which the moving object is predicted to travel at theintersection, based on the dynamic state and the intersectioninformation; detecting a speed control point that is a position includedin the specified reference route; generating, based on the dynamic stateand the intersection information, a first speed model representing atemporal change in a predicted speed of the moving object up to thespeed control point and having the speed at the speed control pointbeing locally minimized, and a second speed model representing atemporal change in the predicted speed of the moving object after thespeed control point and having the speed at the speed control pointbeing locally minimized; and generating and displaying, on a display ofa vehicle, a predicted trajectory of the moving object, based on thefirst speed model and the second speed model, wherein the step ofgenerating of the first speed model further includes estimating aninitial estimated speed of the moving object at the speed control point;and optimizing the first speed model to reduce an error with respect tothe initial estimated speed of the moving object at the speed controlpoint and the observation data of the speed of the moving object atleast at the one point in time.
 16. A non-transitory computer-readablemedium containing a computer program that, when executed by a computer,causes the computer to execute a method comprising: acquiring a dynamicstate relating to traveling of a moving object entering an intersection,the dynamic state including observation data of a speed of the movingobject at least at one point in time; acquiring intersection informationindicating a configuration of the intersection; specifying a referenceroute along which the moving object is predicted to travel at theintersection, based on the dynamic state and the intersectioninformation; detecting a speed control point that is a position includedin the specified reference route; generating, based on the dynamic stateand the intersection information, a first speed model representing atemporal change in a predicted speed of the moving object up to thespeed control point and having the speed at the speed control pointbeing locally minimized, and a second speed model representing atemporal change in the predicted speed of the moving object after thespeed control point and having the speed at the speed control pointbeing locally minimized; and generating and displaying, on a display ofa vehicle, a predicted trajectory of the moving object, based on thefirst speed model and the second speed model, wherein the step ofgenerating the first speed model further includes estimating an initialestimated speed of the moving object at the speed control point; andoptimizing the first speed model to reduce an error with respect to theinitial estimated speed of the moving object at the speed control pointand the observation data of the speed of the moving object at least atthe one point in time.
 17. The vehicle of claim 14, wherein theprocessing circuitry is further configured to generate the first andsecond speed models of the temporal change of the predicted speed of themoving object, which is an object other than the vehicle.
 18. Thevehicle of claim 14, wherein the processing circuitry is furtherconfigured to acquire the dynamic state of the moving object, which isan object other than the vehicle, from sensors located on the vehicle,at a time when the moving object is entering the intersection.